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Explores semiparametric inference for missing-not-at-random data, addressing challenges in statistical analysis and proposing a doubly-robust estimator.
Explores the importance of causality for robust machine learning, covering ideal datasets, missing data problems, graphical models, and interference models.
Explores data handling fundamentals, including models, sources, and wrangling, emphasizing the importance of understanding and addressing data problems.
